High frequency trading models website

www.ebook3000.com P1: JYS fm JWBT367-Ye October 12, 2010 15:38 Printer: Yet to come iv P1: JYS fm JWBT367-Ye October 12, 2010 15:38 Printer: Yet to come High-Frequency Trading Models GEWEI YE, Ph.D John Wiley & Sons, Inc www.ebook3000.com i P1: JYS fm JWBT367-Ye Copyright October 12, 2010 C 15:38 Printer: Yet to come 2011 by Gewei Ye All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose No warranty may be created or extended by sales representatives or written sales materials The advice and strategies contained herein may not be suitable for your situation You should consult with a professional where appropriate Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002 Wiley also publishes its books in a variety of electronic formats Some content that appears in print may not be available in electronic books For more information about Wiley products, visit our web site at www.wiley.com Library of Congress Cataloging-in-Publication Data: Ye, Gewei, 1971– High-frequency trading models / Gewei Ye p cm – (Wiley trading series) Includes bibliographical references and index ISBN 978-0-470-63373-1 (cloth) Investment analysis Speculation–Mathematical models Portfolio management–Mathematical models Financial engineering I Title HG4529.Y42 2011 332.64 501–dc22 2010024731 Printed in the United States of America 10 ii P1: JYS fm JWBT367-Ye October 12, 2010 15:38 Printer: Yet to come To my parents, Lei, Jessica, and friends www.ebook3000.com iii P1: JYS fm JWBT367-Ye October 12, 2010 15:38 Printer: Yet to come iv P1: JYS fm JWBT367-Ye October 12, 2010 15:38 Printer: Yet to come Contents Preface xi Acknowledgments PART I xiv Revenue Models of High-Frequency Trading CHAPTER High-Frequency Trading and Existing Revenue Models What Is High-Frequency Trading? Why High-Frequency Trading Is Important Major High-Frequency Trading Firms in the United States Existing Revenue Models of High-Frequency Trading Operations Categorizing High-Frequency Trading Operations Conclusion CHAPTER 10 Roots of High-Frequency Trading in Revenue Models of Investment Management 13 Revenue Model 1: Investing 14 Revenue Model 2: Investment Banking 17 Revenue Model 3: Market Making 18 Revenue Model 4: Trading 18 Revenue Model 5: Cash Management 19 Revenue Model 6: Mergers and Acquisitions 20 Revenue Model 7: Back-Office Activities 20 v www.ebook3000.com P1: JYS fm JWBT367-Ye October 12, 2010 15:38 Printer: Yet to come CONTENTS vi Revenue Model 8: Venture Capital 20 Creating Your Own Revenue Model 21 How to Achieve Success: Four Personal Drivers 22 Conclusion 27 CHAPTER History and Future of High-Frequency Trading with Investment Management 29 Revenue Models in the Future 30 Investment Management and Financial Institutions 31 High-Frequency Trading and Investment Management 32 Technology Inventions to Drive Financial Inventions 34 The Ultimate Goal for Models and Financial Inventions 34 Conclusion 37 PART II Theoretical Models as Foundation of Computer Algos for High-Frequency Trading CHAPTER Behavioral Economics Models on Loss Aversion 39 41 What Is Loss Aversion? 41 The Locus Effect 41 Theory and Hypotheses 45 Study 1: The Locus Effect on Inertia Equity 49 Study 2: Assumption A1 and A2 51 General Discussion 53 Conclusion 55 CHAPTER Loss Aversion in Option Pricing: Integrating Two Nobel Models 57 Demonstrating Loss Aversion with Computer Algos 57 Visualizing the Findings 59 Computer Algos for the Finding 61 Explaining the Finding with the Black-Scholes Formula 63 Conclusion 64 P1: JYS fm JWBT367-Ye October 12, 2010 15:38 Printer: Yet to come Contents CHAPTER vii Expanding the Size of Options in Option Pricing 65 The NBA Event 66 Web Data 67 Theoretical Analysis 69 The NBA Event and the Uncertainty Account 72 Controlled Offline Data 77 General Discussion 80 Conclusion 82 CHAPTER Multinomial Models for Equity Returns 85 Literature Review 87 A Computational Framework: The MDP Model 89 Implicit Consumer Decision Theory Empirical Approaches 94 102 Analysis 1: Examination of Correlations and a Regression Model 102 Analysis 2: Structural Equation Model 106 Contributions of the ICD Theory 111 Conclusion 115 CHAPTER More Multinomial Models and Signal Detection Models for Risk Propensity 117 Multinomial Models for Retail Investor Growth 117 Deriving Implicit Utility Functions 131 Transforming Likeability Rating Data into Observed Frequencies 140 Signal Detection Theory 143 Assessing a Fund’s Performance with SDT 146 Assessing Value at Risk with Risk Propensity of SDT for Portfolio Managers 147 Defining Risk Propensity Surface 148 Conclusion 149 www.ebook3000.com P1: JYS fm JWBT367-Ye October 12, 2010 15:38 Printer: Yet to come CONTENTS viii CHAPTER Behavioral Economics Models on Fund Switching and Reference Prices 151 What Is VisualFunds for Fund Switching? 151 Behavioral Factors That Affect Fund Switching 152 Theory and Predictions 157 Study 1: Arbitrary Anchoring on Inertia Equity 164 Study 2: Anchor Competition 166 Study 3: Double Log Law 169 Conclusion 179 PART III A Unique Model of Sentiment Asset Pricing Engine for Portfolio Management 181 CHAPTER 10 A Sentiment Asset Pricing Model 185 What Is the Sentiment Asset Pricing Engine? 185 Contributions of SAPE 187 Testing the Effectiveness of SAPE Algos 190 Primary Users of SAPE 191 Three Implementations of SAPE 191 SAPE Extensions: TopTickEngine, FundEngine, PortfolioEngine, and TestEngine Summary on SAPE 193 194 Alternative Assessment Tools of Macro Investor Sentiment 194 Conclusion 200 CHAPTER 11 SAPE for Portfolio Management — Effectiveness and Strategies 201 Contributions of SAPE to Portfolio Management 202 Intraday Evidence of SAPE Effectiveness 203 Trading Strategies Based on the SAPE Funds 206 Case Study 1: Execution of SAPE Investment Strategies 206 Case Study 2: The Trading Process with SAPE 214 Case Study 3: Advanced Trading Strategies with SAPE 217 P1: OTA ref JWBT367-Ye 308 October 21, 2010 14:50 Printer: Yet to come REFERENCES Kruger, J., and D Dunning 1999 Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments Journal of Personality and Social Psychology 77: 1121–1134 Lazarus, R 1991 Cognition and motivation in emotion American Psychologist 46 (4): 352–367 Lee, A 2002 Effects of implicit memory on memory-based versus stimulus-based brand choice Journal of Marketing Research 39: 440–456 Lee, A., and A Labroo 2004 The effects of conceptual and perceptual fluency on brand evaluation Journal of Marketing Research XLI: 151–165 Lei, V., C Noussair, and C Plott 2001 Nonspeculative bubbles in experimental asset markets: Lack of common knowledge of rationality vs actual irrationality Econometrica 69: 831–859 Leibenluft, J 2008 $596 trillion! http://www.slate.com/id/2202263 Slate.com Leising, M., and T Seeley May 13, 2009 Geithner urges electronic OTC derivatives trading Bloomberg news, http://www.bloomberg.com/apps/news?pid= 20601009&sid=a1Yy1SLxmCwo Loewenstein, G 2001 The creative destruction of decision research Journal of Consumer Research 28 (3): 499–505 Loewenstein, G., E Weber, C Hsee, and N Welch 2001 Risk as feelings Psychological Bulletin 127 (2): 267–286 Markowitz, H.M 1952 Portfolio selection Journal of Finance (1): 77–91 McFadden, D 1999 Rationality for economists? Journal of Risk and Uncertainty 19: 73–105 Mellers, B 2000 Choice and the relative pleasure of consequences Psychological Bulletin 126 (6): 910–924 Monroe, K 1971 Psychophysics of pricing: A reappraisal Journal of Marketing Research (2): 248–250 Monroe, K 2003 Pricing: Making profitable decisions 3rd ed New York: McGrawHill Morrin, M., J Jacoby, G Venkataramani Johar, H Xin, and D Mazursky 2002 Taking stock of stockbrokers: Exploring momentum versus contrarian investor strategies and profiles Journal of Consumer Research 29: 188–199 Mussweiler, T., and F Strack 2001 The semantics of anchoring Organizational Behavior and Human Decision Processes 86: 234–255 NASDAQ 2009 Flash Functionalities http://www.nasdaqtrader.com/content/ ProductsServices/Trading/Flash factsheet.pdf Nijkamp, J., H.J Gianotten, and W.F van Raaij 2002 The structure of consumer confidence and real value added growth in retailing in the Netherlands The International Review of Retail, Distribution and Consumer Research Nunes, J.C., and P Boatwright 2004 Incidental prices and their effect on willingness to pay Journal of Marketing Research 41 (1): 457–466 P1: OTA ref JWBT367-Ye October 21, 2010 14:50 Printer: Yet to come References 309 Nunes, J., and C.W Park 2003 Incommensurate resources: Not just more of the same Journal of Marketing Research 40: 26–38 O’Donoghue, T., and T Banin In press Self awareness and self control In Now or later: Economic and psychological perspectives on intertemporal choice, ed R Baumeister, G Loewenstein, and D Read New York: Russell Sage Foundation Press Papoulis, A 1984 Probability, random variables, and stochastic processes 2nd ed New York: McGraw-Hill Patterson, S., and G Rogow 2009 What’s behind high-frequency trading Wall Street Journal (August 1), http://online.wsj.com/article/SB124908601669298293 html Pratkanis, A.R., and A.G Greenwald 1988 Recent perspectives on unconscious processing: Still no marketing applications Psychology & Marketing (4): 337–353 Pronin, E., J Kruger, K Savitsky, and L Ross 2001 You don’t know me, but I know you: The illusion of asymmetric insight Journal of Personality and Social Psychology 81: 639–656 Rabin, M 1998 Psychology and economics Journal of Economic Literature 36: 11–46 Rayport, J., and B Jaworski 2003 e-Commerce strategies 2nd ed New York: McGraw-Hill/Irwin Riefer, D.M., and W.H Batchelder 1988 Multinomial modeling and the measurement of cognitive processes Psychological Review 3: 318–339 Roediger, H.L., III 1990 Implicit memory: Retention without remembering American Psychologist 45: 1043–1056 Rust, R., K.N Lemon, and D Narayandas 2005 Customer equity management Upper Saddle River, NJ: Prentice Hall Rust, R.T., K.N Lemon, and V.A Zeithaml 2004 Return on marketing: Using customer equity to focus marketing strategy Journal of Marketing 68: 109–127 Rust, R., C Moorman, and P.R Dickson 2002 Getting return on quality: Revenue expansion, cost reduction, or both Journal of Marketing 66: 7–24 Samuelson, P.W., and R Zeckhauser 1988 Status quo bias in decision making Journal of Risk and Uncertainty 1: 7–59 Saunders, A., and M Cornett 2008 Financial institutions management: A risk management approach New York: McGraw-Hill/Irwin Schacter, D.L 1987 Implicit memory: History and current status Journal of Experimental Psychology: Learning, Memory, and Cognition 13: 501–518 Schacter, D.L., and R Badgaiyan 2001 Neuroimaging of priming: New perspectives on implicit explicit memory Current Directions of Psychological Science 10 (1): 1–4 Schiffman, L., and L Kanuk 2001 Consumer Behavior Upper Saddle River, NJ: Prentice Hall www.ebook3000.com P1: OTA ref JWBT367-Ye 310 October 21, 2010 14:50 Printer: Yet to come REFERENCES Shankar, V., and R.N Bolton 2004 An empirical analysis of determinants of retailer pricing strategy Marketing Science 23 (1): 28–49 Sharpe, W 1992 Asset allocation: Management style and performance measurement Journal of Portfolio Management (Winter): 7–19 Sharpe, W 2000 Portfolio theory and capital markets New York: McGraw-Hill Sharpe, W., G.J Alexander, and J.V Bailey 1999 Investments Upper Saddle River, NJ: Prentice Hall Sharpe, W.F 1963 A simplified model of portfolio analysis Management Science 9: 277–293 Sharpe, W.F 1964 Capital asset prices: A theory of market equilibrium under conditions of risk Journal of Finance 19 (3): 425–442 Shiller, R 2000a Irrational exuberance Princeton, NJ: Princeton University Press Shiller, R 2000b Measuring bubble expectations and investor confidence Journal of Psychology and Financial Markets (1): 49–60 Shiv, B., and A Fedorikhin 1999 Heart and mind in conflict: The interplay of affect and cognition in consumer decision making Journal of Consumer Research 26 (December): 278–292 Simon, H 1955 A behavioral model of rational choice Quarterly Journal of Economics 69: 99–118 Simonson, I 1989 Choice based on reasons: The case of attraction and compromise effects Journal of Consumer Research 16: 158–174 Simonson, I., Z Carmon, R Dhar, A Drolet, and S.M Nowlis 2001 Consumer research: In search of identity Annual Review of Psychology 52: 249–275 Simonson, I., and A Drolet 2004 Anchoring effects on consumers’ willingnessto-pay and willingness-to-accept Journal of Consumer Research 31 (4): 681– 690 Simonson, I., T Kramer, and M.J Young 2004 Effect propensity Organizational Behavior and Human Decision Processes 95 (2): 156–174 Snodgrass and Corwin 1988 Perceptual identification thresholds for 150 fragmented pictures from the Snodgrass and Vanderwart picture set Perceptual and Motor Skills 67: 3–36 Sommerville, I 1996 Software engineering 5th ed Harlow, England: AddisonWesley Spicer, J., and J Kwan 2009 High-frequency trading surges across the globe Reuters, http://www.reuters.com/article/idUSTRE5B110520091202 Srivastava, R.K., T.A Shervani, and L Fahey 1999 Marketing, business processes, and shareholder value: An organizationally embedded view of marketing activities and the discipline of marketing Journal of Marketing 63: 168–180 Srull, T.K., and R Wyer 1989 Memory and cognition in its social context Hillsdale, NJ: Erlbaum Stevens, S.S 1961 To honor Fechner and repeal his law Science 133: 80–86 P1: OTA ref JWBT367-Ye October 21, 2010 14:50 Printer: Yet to come References 311 Swensen, D 2000 Pioneering portfolio management: An unconventional approach to institutional investment New York: Free Press Swensen, D 2005 Unconventional success: A fundamental approach to personal investment New York: Free Press Tabachinick, B., and L Fidell 1996 Using multivariate statistics 3rd ed HarperCollins College Publishers Tashchian, A., D White, and S Pak 1988 Signal detection analysis and advertising recognition: An introduction to measurement and interpretation issues Journal of Marketing Research 25: 397–404 Thaler, R 1980 Toward a positive theory of consumer choice Journal of Economic Behavior and Organization 1: 39–60 Thaler, R 1985 Mental accounting and consumer choice Marketing Science (3): 199–214 Thaler, R 1991a Mental accounting matters Journal of Behavioral Decision Making 12: 183–206 Thaler, R 1991b The winner’s curse: Paradoxes and anomalies of economic life Princeton, NJ: Princeton University Press Thaler, R.H 1993 Advances in behavioral finance: Introduction Advances in Behavioral Finance, xv–xxi New York: Russell Sage Foundation Thaler, R 1999 The end of behavioral finance Financial Analysts Journal (November): 12–17 Tversky, A., and D Kahneman 1974 Judgment under uncertainty: Heuristics and bias Science 185: 1124–1130 Tversky, A., and D Kahneman 1992 Advances in prospect theory: Cumulative representations of uncertainty Journal of Risk and Uncertainty 5: 297–323 van Heerde, H., S Gupta, and D.R Wittink 2003 Is 75% of the sales promotion bump due to brand switching? No, only 33% is Journal of Marketing Research XL (November): 481–491 van Raaij, F 1984 Micro and macro economic psychology Journal of Economic Psychology (4): 385–401 van Raaij, W.F 1989 How consumers react to advertising International Journal of Advertising 8: 261–273 van Raaij, W.F., and H.J Gianotten 1990 Consumer confidence, expenditure, saving, and credit Journal of Economic Psychology 11 (2): 269–290 von Neumann, J., and O Morgenstern 1944 Theory and games and economic behavior Princeton, NJ: Princeton University Press Wyer, R.S., Jr., and T.K Srull 1986 Human cognition in its social context Psychological Review 93 (July): 322–359 Xu, M., and F Bellezza 2001 A comparison of the multimemory and detection theories of know and remember recognition judgments Journal of Experimental Psychology: Learning, Memory, and Cognition 27: 1197–1210 www.ebook3000.com P1: OTA ref JWBT367-Ye 312 October 21, 2010 14:50 Printer: Yet to come REFERENCES Ye, G 2003 Formulas of decision service and the process of using the formulas Patent of United States Patent and Trademark Office (No 09/852,337) Ye, G 2005 The locus effect on inertia equity Journal of Product and Brand Management 14 (3): 206–210 Ye, G.W 2000 Modeling the unconscious components of marketing communication Dissertation, Tilburg University, The Netherlands Ye, G.W 2002 Formulas of intelligent decision service and the process of using the computerized formulas United States Patent and Trademark Office, publication reference number 30280172, approved December 2002 Ye, G., and W.F van Raaij 1997 What inhibits the mere-exposure effect: Recollection or familiarity? Journal of Economic Psychology 18: 629–648 Ye, G., and W.F van Raaij 2002 A multinomial decision process model with implicit and affective components Unpublished manuscript Ye, G.W., and W.F van Raaij 2003 Feeling bias as status quo: A signal detection analysis Unpublished manuscript Ye, G.W., and W.F van Raaij 2005 Modeling preference formation: A multinomial decision process model Unpublished manuscript Yonelinas, A.P 1994 Receiver-operating characteristics in recognition memory: Evidence for a dual-process model Journal of Experimental Psychology: Learning, Memory, and Cognition 20: 1341–1354 Yonelinas, A.P 1999 The contribution of recollection and familiarity to recognition and source-memory judgments: A formal dual-process model and an analysis of receiver operating characteristics Journal of Experimental Psychology: Learning, Memory, & Cognition 25: 1415–1434 Zajonc, R.B 1968 Attitudinal effects of mere exposure Journal of Personality and Social Psychology Monograph Supplement (2, Pt 2): 1–27 Zajonc, R.B., and H Markus 1982 Affective and cognitive factors in preference Journal of Consumer Research 9: 123–131 P1: OTA ata JWBT367-Ye August 19, 2010 21:30 Printer: Yet to come About the Author r Gewei Ye is the president of Yeswici.com, a research platform for investment modeling and computing He teaches graduate-level courses in financial engineering, derivatives, institutions, portfolio management, and program trading strategies at Johns Hopkins University, a well-respected training camp With the teaching notes that are part of this book, he is training Hopkins graduate students to be top portfolio managers, high-frequency trading technologists, quantitative traders, financial advisors, and so forth He hopes that in the future some of these Hopkins graduates will be very successful in investment management and choose to give back to society His research interests include high-frequency trading and behavioral investing with the Sentiment Asset Pricing Engine (SAPE), option pricing in stocks and bonds, and behavioral economics for investment research He earned a PhD from the University of Tilburg, the Netherlands (ranked top in economics and business research in Europe) He has published about 40 articles in peer-reviewed journals or conference proceedings He has spent more than 10 years building financial models and computing systems Recently he has released the SAPE, a Web-based strategy builder for algorithmic trading and high-frequency trading systems For a live demo on SAPE, see Yeswici.com The SAPE algorithms have been used to construct SAPE funds that have repeatedly outperformed the market indexes Dr Ye has been a senior architect and consultant for investment and technology companies and agencies including Citigroup, IBM, T Rowe Price, and Federal Reserve Banks D 313 www.ebook3000.com P1: OTA ata JWBT367-Ye August 19, 2010 21:30 Printer: Yet to come 314 P1: JYS ind JWBT367-Ye October 20, 2010 11:35 Printer: Yet to come Index Abstraction, 14 Advanced trading strategies with SAPE Black-Scholes model, 290–292 large cap hedge strategy, 219 large cap long only strategy, 217–219 long short strategy, 219–221 potential assets under management, 219 summary, 221 Algorithms (algos) computer algo development, 248–256 computer algos for finding, 61–63 demonstration of loss aversion in option pricing, 57–59 Java programming, algo jump-starting with, 266–273 PHP programming for algo development, 256–266 and portfolio management with SAPE, 222 for sentiment asset pricing engine (SAPE), effectiveness of, 190–191 technology infrastructure for algo creation, 245–277 Algorithms (algos) creation for high-frequency trading efficient portfolio frontier, 294–296 flex user interface, 286–290 Monte Carlo simulation, 293–294 net present value (NPV) calculation, 284–286 probability from z scores, 279–281 Sharpe ratio, 282–284 signal detection theory (SDT), 296–298 volatility calculation with ARCH formula, 292–293 z scores from probability, 281–282 summary, 298 Alternative investment tools of macro investor sentiment about, 194–197 development process, 197–198 web system contributions, 199 web system functions, 199 American options, 231–232 Anchor competition, 162–164 Anchor competition study, 166–169 conclusion, 169 design, 167 discussion and analysis, 168 participants, 166–167 results, 167–168 Anchor prices and double log law, 177–178 Anchoring See also arbitrary anchoring effect; arbitrary anchoring on inertia equity study and uncertainty, 71–72 of the value of endowment effect, 155–156 Anchoring effect limits, 155–156 Anchoring effects, 154–155 Anchoring price and the locus effect, 48 Arbitrage and hedging strategy evaluation, 212–213 315 www.ebook3000.com P1: JYS ind JWBT367-Ye October 20, 2010 11:35 316 Arbitrary anchoring effect boundaries of, 176–177 robustness of, 176 Arbitrary anchoring on inertia equity study conclusion, 166 design, 164–165 discussion and analysis, 165–166 participants, 164 results, 165 Arrays converting an arraylist, 273 element deletion, 262 extreme values, 264 iterating, 262 merging, 263 searching, 263 into strings, 263 using, 261 Arrays and arraylist data storage, 273 Base conversion, 260 Basic strategy, 215 Bayesian theorem, new growth function, 124–125 Behavioral economics models on fund switching and reference prices arbitrary anchoring on inertia equity, 164–166 behavioral factors that affect fund switching, 152–157 inertia equity, theory and production, 157–164 Visual Funds for fund switching, 151–152 summary, 179 Behavioral economics models on loss aversion, 41–55 anchoring price and the locus effect, 48 assumption study, 51–53 future research, 54–55 locus effect on inertia equity study, 49–51 self-other asymmetry and loss aversion, 45–46 Printer: Yet to come INDEX theoretical implications, 53–54 theory and hypothesis, 45–48 summary, 55 Behavioral factors that affect fund switching about, 152–153 anchoring effect limits, 155–156 anchoring effects, 154–155 anchoring the value of endowment effect, 155–156 endowment effect, 153–154 Black-Scholes model, 15, 65, 85, 186, 238–239, 290–292 Brand switching, 153, 175–176 Breakeven analysis, 216–217 C++ programming, 273 Call options, 58–60, 186, 232–236, 238 See also American options; European options Choice reversal, 70 CLV equation and CE equation of RLZ model, 120–121 Computer algo development, 248–256 Java, appearance of, 253–254 Java, object-oriented features of, 250–252 PHP application development, 254–256 PHP programming for algo development, 256–266 programming languages, 250 Consumer confidence, 97 Correlations and regression model, 102–106 correlations, examination of, 104 discussion, 106 empirical hypothesis, 103 methods of, 103–104 multiple regression analysis, 104–106 Cost analysis, 216 Credit default swaps, 228 Customer equity, 160 Customer retention, 119 Customer retention rate, 130 P1: JYS ind JWBT367-Ye October 20, 2010 11:35 Printer: Yet to come Index 317 Date and time, 260–261 Dedicated web server setup, 246–248 Derivatives See also options about, 227–228 behavioral economics, behavioral investing based on, 243–244 Black-Scholes model as special case of binomial model, 237 credit default swaps, 230–231 forwards and futures, 240–241 implied volatility, 238 interest rate swap pricing with prospect theory, 241–243 mortgage-backed securities, 229–230 options, benefits of, 234 options, financial instruments for writing, 236–237 options, profiting with, 234–235 options and option values, 231–234 volatility over time, 239–240 volatility smile, 238–239 summary, 244 Detection models for risk propensity, 117–149 Development environment, 267 Double log law, anchor prices and, 177–178 Double log law study alternative models, 171–173 background, 169–170 conclusion, 173–174 data, 171 discussion, 174–179 hypothesis, 170 results, 171 Dropping table, 275 Efficient portfolio frontier, 294–296 Endowment effect anchoring the value of, 155–156 behavioral factors that affect fund switching, 153–154 in brand switching, 175–176 inertia equity to assess, 174–175 inertia equity to assess value of, 160–162 to model brand switching, 158–160 European options, 231–232, 238, 298 Existing revenue models using high-frequency trading, 8–9 Explicit components, 88 Explicit consumer decision theory consumer confidence, implicit components of, 97 consumer constructs and financial metrics, 98 ICD theory, 94–96 ICD theory, theoretical foundation of, 96 implicit consumer confidence ratios, 99–102 stock returns, 98–99 stock returns, implicit investment decisions underlying, 97–98 Exponents, 260 Extended hedging strategy, 207–212 Fechner’s law, 170, 172, 173 Flex programming, 274 Flex user interface, 286–290 Floating point numbers, 259, 271 Functions arguments and results, 265 global variables inside, 266 use of, 264 Fund identification, 207 Fund performance analysis, 146 Fund selection, 215–216 Fund strategy, 215 Fund switching behavioral economics models on, 151–179 behavioral factors that affect, 152–157 Visual Funds for, 151–152 Future data, 203 Greenspan effect, 99 Growth function MATLAB to draw, 125–126 new, and Bayesian theorem, 124–125 prediction and implication, 126–127 Guessing components, 89 www.ebook3000.com P1: JYS ind JWBT367-Ye October 20, 2010 11:35 318 Hash table vs hashmap, 273 Hedging strategies, 213 High-frequency trading See also origins of high-frequency trading categorization of operations, 9–10 definition of, 3–5 existing revenue models, 8–9 importance of, 5–6 and investment management, 32–33 and technology, 222–223 technology inventions, 34 ultimate goals for models and financial inventions, 34–37 U.S firms, 6–7 summary, 10–11, 37 High-frequency trading, history and future with investment management, 29–37 investment management and financial institutions, 31–32 revenue models in future, 30–31 High-frequency trading models and existing revenue models, 3–11 new, 225–226 Hindsight bias, 132 Hyperbolic absolute risk aversion (HAFA), 136 Implicit components, 87–88 Implicit consumer decision (ICD) measures interpretation of MDP model, 93–94 MDP model, 92 transformation to MDP model, 92–93 Implicit consumer decision (ICD) theory contributions behavioral finance with aggregate perspective, 113–114 consumer decision making, implicit components, 111–112 consumer sentiment and financial performance, 112–113 Implicit investor sentiment, 104 Implicit memory, 87–88, 134 Printer: Yet to come INDEX Implicit utility derivation, 131–139 implicit utility function, 136–139 investor rating data into observed frequency, 135–136 MDP model equations, 134 Inertia equity arbitrary anchoring on, 164–166 to assess endowment effect, 174–175 investment implications, 178 Inertia equity, theory and production anchor competition accounts for, 162–164 to assess value of endowment effect, 160–162 endowment effects to model brand switching, 158–160 Inertia ratio, 169 Inertia value, 158 Investing revenue model abstraction in investing and trading, 14–15 common investing vehicles, 15–17 Investor sentiment, 194–199 Irrational choice, 74–76 Java programming, algo jump-starting with about, 266–267 arrays, converting an arraylist into, 273 arrays and arraylist, data storage from, 273 development environment, 267 floating numbers, rounding, 271 hash table vs hashmap, 273 primitive numbers, integer object conversion, 271 random number generation, 271–272 sentences into words, 269 strings, assembly of, 269–270 strings, blank removal of, 270 strings, controlling cases of, 270 strings, data object creation from, 273 strings, validity check of, 270–271 substring extraction, 268–269 P1: JYS ind JWBT367-Ye October 20, 2010 11:35 Printer: Yet to come Index 319 Likeability rating data into observed frequencies implicit process properties, 141–143 implicit utility function, 140 implicit utility function properties, 142–143 value functions properties, 140 Locus effect, 41–45 Log inertia equity, 170 Logarithms, 259 Loss aversion See also behavioral economics models on loss aversion definition of, 41 irrational choice and, 75–76 Loss aversion in option pricing algo (algorithm) demonstration of, 57–59 Black-Scholes formula, 63–64 computer algos for finding, 61–63 visualization of, 59–61 summary, 63 Managerial decisions, 130 Market neutrality, 219 MATLAB, 125–126 Modern portfolio theory (MPT), 16, 182, 186–187, 201, 203, 283 Money formatting, 260 Monte Carlo simulation, 293–294 Mortgage-backed securities, 228 MPT model to decompose brand-switching matrix, 121–123 Multinomial decision process (MDP) model, 89–94 ICD measures, 92 ICD measures, interpretation of, 93–94 ICD measures, transformation to, 92–93 Multinomial models and detection models fund performance analysis, 146 implicit utility derivatives, 131–139 likeability rating data into observed frequencies, 140–143 for risk propensity, 117–149 risk propensity definition, 148–149 risk propensity of SDT, 147–148 signal detection theory (SDT), 143–146 value at risk analysis, 147–148 Multinomial models for equity returns about, 85–87 affective components, 88–89 correlations and regression model, 102–106 empirical approaches, 102 explicit components, 88 explicit consumer decision theory, 94–102 guessing components, 89 ICD theory contributions, 111–114 implicit components, 87–88 literature review, 87–89 MDP model, 89–94 structural equations model, 106–111 summary, 115 Multinomial models for retail investor growth about, 117–119 Bayesian theorem, new growth function with, 124–125 CLV equation and CE equation of RLZ model, 120–121 growth function, MATLAB to draw, 125–126 growth function, prediction and implication, 126–127 managerial decisions, 130 MPT model to decompose brand-switching matrix, 121–123 new growth development, 119–129 peak analysis, 127 peak growth rate, 127 retention rate and market share, 127–129 theoretical implications, 129 Multinomial processing tree model, 89–94 www.ebook3000.com P1: JYS ind JWBT367-Ye October 20, 2010 11:35 320 NBA (National Basketball Association) event and uncertainty account irrational choice and choice anomalies, 72–75 irrational choice and loss aversion, 75–76 summary, 75–76 Net present value (NPV) calculation, 284–286 New growth development, 119–129 Non-parametric SDT, 145–146 Option pricing See also loss aversion in option pricing anchoring and uncertainty, 71–72 controlled offline data, 77–80 general discussion, 80–82 managerial implications, 81–82 NBA event, 66–67 NBA event and uncertainty account, 72–76 option size expansion, 65–83 procedure and choice reversal, 70 switchers, 71 theoretical analysis, 69 theoretical implications, 80–81 web data, 67–69 summary, 82–83 Options American options, 231–232 benefits of derivatives, 234 European options, 231–232, 238, 298 financial instruments for writing derivatives, 236–237 and option values, 231–234 profiting with derivatives, 234–235 Origins of high-frequency trading about, 13–14 back-office revenue model, 20 cash management revenue model, 19 investing revenue model, 14–17 investment banking revenue model, 17–18 market making revenue model, 18 merger and acquisition revenue model, 20 new revenue model creation, 21–22 Printer: Yet to come INDEX personal success drivers, 22–26 trading revenue model, 18–19 venture capital revenue model, 20–21 summary T, 27 OTC derivatives, 226, 227 Ownership bias, 176 Passing value by reference, 264–265 Peak analysis, 127 Peak growth rate, 127 Personal success drivers, 22–26 PHP and HTML, 257 PHP file location, 257 PHP files on web browsers, 257–258 PHP programming for algo development, 256–266 arrays, element deletion, 262 arrays, extreme values in, 264 arrays, iterating through, 262 arrays, merging, 263 arrays, searching in, 263 arrays, using, 261 arrays into strings, 263 base conversion, 260 date and time, 260–261 exponents, 260 floating point numbers, 259 functions, arguments and results, 265 functions, global variables inside of, 266 functions, use of, 264 logarithms, 259 money formatting, 260 passing value by reference, 264–265 PHP and HTML, 257 PHP file location, 257 PHP files on web browsers, 257–258 random number creation, 259 substrings, extracting, 258 substrings, locating, 258 variables, checking, 259 Portfolio management with SAPE algos, 222 Preference bias indicator, 145 P1: JYS ind JWBT367-Ye October 20, 2010 11:35 Printer: Yet to come Index 321 Primitive numbers, integer object conversion, 271 Probability from z scores, 279–281 Program trading, 29 Psychophysics laws, 170, 177 Put options, 4, 58, 61, 186, 207, 213, 232–237 See also American options; European options Random number creation, 259 Random number generation, 271–272 Records, updating, 276 Reference prices, 151–179 Research using signal detection theory (SDT), 179 Retention rate and market share, 127–129 Revenue models back-office, 20 cash management, 19 creation of new, 21–22 existing, using high-frequency trading, 3–11 future, 30–31 investing, 14–17 investment banking, 17–18 market making, 18 merger and acquisition, 20 trading, 18–19 venture capital, 20–21 Risk propensity definition, 148–149 of SDT, 147–148 RLZ model, 120–121 SAPE and high-frequency trading, 221–223 high-frequency trading and technology, 222–223 portfolio management with SAPE algos, 222 SAPE for portfolio management about, 201–203 advanced trading strategies with SAPE, 217–221 intraday evidence of effectiveness of, 203–205 SAPE and high-frequency trading, 221–223 SAPE investment strategy study, 206–214 trading process with SAPE study, 214–217 trading strategies using SAPE funds, 206 summary, 223 SAPE investment strategy study about, 206–207 arbitrage and hedging strategy evaluation, 212–213 extended hedging strategy, 207–212 fund identification, 207 plans, 207 summary, 213–214 Select statement, 275 Sentences into words, 269 Sentiment asset pricing engine (SAPE) See also advanced trading strategies with SAPE; SAPE for portfolio management; SAPE investment strategy study; trading process with SAPE study alternative investment tools of macro investor sentiment, 194–199 contribution of, 187–190 described, 185–187 effectiveness of algos for, 190–191 engines built on, 193–194 implementations of, 191–193 primary users of, 191 summary on, 194 Sentiment asset pricing engine (SAPE) for portfolio management, 181–183 Sharpe ratio, 146, 202, 204, 282–284 Sigma (standard deviation volatility), 147, 238 Signal detection theory (SDT), 143–144, 296–298 non-parametric SDT, 145–146 research using, 179 www.ebook3000.com P1: JYS ind JWBT367-Ye October 20, 2010 11:35 Printer: Yet to come INDEX 322 SQL (structured query language) dropping table, 275 records, updating, 276 select statement, 275 table creation, 274–275 tables, data insertion, 275 tables, record deletion, 275 Status quo bias, 42–46, 49, 75–76, 153, 155, 159 See also endowment effect; inertia equity Stevens’ law, 153, 172–174, 178, 179 Strings assembly, 269–270 blank removal, 270 controlling cases, 270 data object creation, 273 validity check, 270–271 Structural equations model, 106–111 discussion, 110–111 empirical hypothesis, 107–109 methods of, 108–109 results, 109–110 Substrings extracting, 258, 268–269 locating, 258 Swaps, 228 See also credit default swaps Tables creation of, 274–275 data insertion, 275 record deletion, 275 Tangency portfolio, 203 Technology infrastructure for algo creation, 245–277 C++ programming, 273 computer algo development, 248–256 dedicated web server setup, 246–248 flex programming, 274 Java programming, algo jump-starting with, 266–273 SQL (structured query language), 274–276 UNIX/LINUX commands for algo development, 276 web hosting vs dedicated web servers, 245–246 summary, 277 Threshold, 178 Trading frequency, 215 Trading process with SAPE study about, 214–215 basic strategy, 215 breakeven analysis, 216–217 cost analysis, 216 fund selection, 215–216 fund strategy, 215 trading frequency, 215 Tranches, 230 UNIX/LINUX commands for algo development, 276 Value at risk analysis, 147–148 Variables, 259 Visual Funds, 151–152 Volatility calculation with ARCH formula, 292–293 sigma (in standard deviation), 147, 238 Web hosting vs dedicated web servers, 245–246 Web Investor Confidence Index (WICI), 197–199 Weber-Fechner’s laws, 170, 173–174 Weber’s law, 43, 154–155, 169–173 Z scores from probability, 281–282 ... Revenue Models of High- Frequency Trading CHAPTER High- Frequency Trading and Existing Revenue Models What Is High- Frequency Trading? Why High- Frequency Trading Is Important Major High- Frequency Trading. .. Existing Revenue Models of High- Frequency Trading Operations Categorizing High- Frequency Trading Operations Conclusion CHAPTER 10 Roots of High- Frequency Trading in Revenue Models of Investment... to come PART I Revenue Models of High- Frequency Trading I n Part I, we cover the introduction to high- frequency trading and the existing revenue models of high- frequency trading In addition, we